Despite their improved performance, the existing deep learning-based methods fail to address the challenges mentioned above very well.
Firstly, when facing the lack of raw node attributes, they do not create informative node encoding to represent the nodes' property. Concretely, the one-hot identity feature in [1]}, [2]} and the random initialized feature in [3]} cannot express any structural or temporal property of each node. The distance-based node labeling strategy in [4]} only considers local structural information, which also has a shortage of expression power.
Furthermore, most of these methods use two individual network modules to extract spatial and temporal features, resulting in the insufficient capability to capture the coupled information. For instance, in AddGraph [3]} and StrGNN [4]}, a Graph Convolutional Network (GCN) module is employed to acquire spatial knowledge, with a following Gated Recurrent Unit (GRU) module captures temporal information. The isolated processing of two types of information results in missing the coupled spatial-temporal features and further leads to a sub-optimal solution.
